It's universally accepted that AI is a game-changer and is having a huge impact on organizations across all industries. In the years to come, this isn't something that is going to change.

Indeed, AI and machine learning will be become so pervasive, making companies more innovative and allowing employees to offload the drier parts of their jobs to machines, that it'll be surprising to find organizations which aren't supported by AI in some way.

In healthcare, AI is used to power through millions of data sets to try to diagnose patients earlier or find cures for diseases. In retail, AI is analyzing customer behaviors and preferences before providing accurate recommendations for other products or services you might like to purchase.

Unsurprisingly, it's also transforming how corporate IT departments operate, by automating repetitive programming and development tasks so technology teams can focus on more strategic, higher value IT initiatives.

But while AI and machine learning dominate business and technology news headlines today, a decade ago the majority of AI advancements were coming out of academia.

The practical, real-world, mainstream application of AI for businesses was fuzzy at best, and few expected it to mature as fast it has such that large enterprises the world over could trust and rely on it to power business-critical business processes.

But as I learned from trailblazing CEOs that came before me, it's wise to play the long-game and invest as much as 10 percent of your corporate R&D budget in 'big bets' that, while speculative at the time, may pay off enormously down the line. For us at SnapLogic, that big bet more than eight years ago was on machine learning.

Many Paths to Innovation

As SnapLogic was getting off the ground, in 2010, our leadership team began investigating how machine learning technologies could potentially be applied to enterprise integration projects, our core business.

It was around that time that I was introduced to Greg Benson, then (and still) a Professor of Computer Science at the University of San Francisco.

While I've always approached data through a business lens, with an emphasis on practicality and usefulness in delivering measurable results for customers, Greg brought a decidedly different academic view, one where hypotheses are tested and experimentation is prized.

Many spirited discussions, sometimes debate, between the two of us ensued.

Months later, I hired Greg to join SnapLogic part-time knowing full well he wanted to continue in his teaching capacity at the University of San Francisco.

In fact, I encouraged this - his academic pursuits and interactions with the up-and-coming generation of technologists were central to the value he could deliver as a SnapLogic employee.

We had just a dozen employees at the time. Some on our team questioned this new hire; as a small startup with little available headcount and budget, we needed to be wise with each and every hire and there were other important roles we urgently needed to fill.

But I knew we wanted to approach things differently at SnapLogic, and to do so we needed bright minds who brought a different perspective.

Greg dug in on machine learning and its potential application to enterprise integration. His work applying machine learning to predictive field linking, a technique to reduce the tedious aspects of building integrations, was instrumental in the early development of our core product.

But, for the right reasons, other pressing product and customer priorities took him away from machine learning for a while. But he would eventually return - more on that later.

Bridging Academia and Industry

Having worked with Greg now for several years, I'm convinced there's tremendous value to be had by bringing together smart, out-of-the-box thinkers from both industry and academia - particularly in the area of AI and machine learning.

AI developed in industry is often done so for very specific use cases, often driven by customer requirements or tied to product or revenue goals, with prescriptive timelines and end-results in mind.

On the other hand, in academia, AI development is principally done for exploratory reasons, to test theories and hypotheses with a desire to see what AI is capable of achieving, without boundaries or limitations.

The two approaches often require different skills sets and serve important, though seemingly unrelated goals. However, as AI becomes more ingrained in our lives, bringing both groups together to share ideas and best practices - and importantly, to share data - is paramount.

In order to continue to drive and accelerate innovation - for the betterment of industry, academia, and society as a whole - we must learn from and work with each other.

AI and machine learning are fresh, urgent, and require seriously considered thinking to bring new ideas to fruition. Academia has out-there-thinking in spades, but not necessarily enough time or resource, or real-world data, to bring some of their ideas to life.

Thankfully, the relationship between academia and industry has been strengthening over recent years.

Universities have been starting to work with AI industry leaders to open doors and create opportunities for those in industry to teach courses and share real-life business cases with their students, and for academics to share their knowledge and contribute to live projects within organizations.

In industry, if we want the next generation of AI experts to flourish we need to support them in their development.

Some graduate computer science teams, for example, those at the University of San Francisco, regularly work with industry leaders in Silicon Valley, SnapLogic included, to put their AI and machine learning theories into practice. Internship programs such as these let students work on real projects but also receive credit for their courses.

It also means they're more employable, and immediately productive employees, as soon as they finish university. Through our own program at SnapLogic, we've hired a number of standout University of San Francisco interns as full-time employees.

When Big Bets Pay Off

About four years ago, I asked Greg to re-focus on machine learning and, with new technology advances surfacing in recent years, how we could apply these to our core product to increase user productivity and time-to-value for our customers.

Much work was done - not a straight line to be sure, for every three steps forward there were a couple steps back - but last year we introduced Iris, our vision, and technology roadmap to bring machine learning to enterprise integration.

The first capability to be delivered under Iris was the SnapLogic Integration Assistant, a recommendation engine that uses machine learning to deliver expert step-by-step guidance for building data pipelines - with up to 90 percent accuracy.

It then applies that learning to improve the speed and quality of integrations across data, applications, and business processes.

The result: our users are more efficient and productive, complex app and data integrations can be completed in a fraction of the time and cost, and IT and business teams can stay focused on delivering meaningful business outcomes.

We're just getting started with Iris, with so much more to come, but out of the gates I'm proud to say it's been a resounding success for us and our customers.

I credit our early success, in part, to the collaborative ideas, approaches, and development work enabled by bringing both our business and academic minds and skill-sets together.

AI is the future. To bring about the future we all want, industry and academia must work together to realize its full potential and truly transform business, education, and society for the better.